initial commit of the RandomForestClassifier

README.md

@@ -2,8 +2,29 @@
 
 This project aims to used text exported ML models generated by sci-kit learn and make them usable in Java.
 
-For example, a DecisionTreeClassifier model trained on the Iris dataset and exported using `sklearn.tree`
-export_text() as shown below:
+## Support
+* The tree.DecisionTreeClassifier is supported
+  * Supports `predict()`,
+  * Supports `predict_proba()` when `export_text()` configured with `show_weights=True`
+* The tree.RandomForestClassifier is supported
+  * Supports `predict()`,
+  * Supports `predict_proba()` when `export_text()` configured with `show_weights=True`
+
+## Installing
+
+### Importing Maven Dependency
+```xml
+<dependency>
+  <groupId>rocks.vilaverde</groupId>
+  <artifactId>scikit-learn-2-java</artifactId>
+  <version>1.0.0</version>
+</dependency>
+```
+
+## DecisionTreeClassifier
+
+As an example, a DecisionTreeClassifier model trained on the Iris dataset and exported using `sklearn.tree`
+`export_text()` as shown below:
 
 ```
 >>> from sklearn.datasets import load_iris
@@ -26,17 +47,8 @@ export_text() as shown below:
 |   |   |--- class: 2
 ```
 
-can be executed in Java Maven. Note that when calling `export_text` it is recommended that `max_depth` be set
-to sys.maxsize so that the tree isn't truncated.
-
-### Importing Maven Dependency
-```xml
-<dependency>
-  <groupId>rocks.vilaverde</groupId>
-  <artifactId>scikit-learn-2-java</artifactId>
-  <version>1.0.0</version>
-</dependency>
-```
+The exported text can then be executed in Java. Note that when calling `export_text` it is 
+recommended that `max_depth` be set to sys.maxsize so that the tree isn't truncated.
 
 ### Java Example
 In this example the iris model exported using `export_tree` is parsed, features are created as a Java Map
@@ -45,7 +57,7 @@ and the decision tree is asked to predict the class.
 ```
     Reader tree = getTrainedModel("iris.model");
     final Classifier<Integer> decisionTree = DecisionTreeClassifier.parse(tree,
-                new PredictionFactory.IntegerPredictionFactory());
+                PredictionFactory.INTEGER);
 
     Map<String, Double> features = new HashMap<>();
     features.put("sepal length (cm)", 3.0);
@@ -57,10 +69,47 @@ and the decision tree is asked to predict the class.
     System.out.println(prediction.toString());
 ```
 
-## Support
-* The tree.DecisionTreeClassifier is supported
-  * Supports `predict()`, 
-  * Supports `predict_proba()` when `export_text()` configured with `show_weights=True`
+## RandomForestClassifier
+
+To use a RandomForestClassifier that has been trained on the Iris dataset, each of the `estimators`  
+in the classifiers need to be and exported using `from sklearn.tree export export_text` as shown below:
+
+```
+>>> from sklearn import datasets
+>>> from sklearn import tree
+>>> from sklearn.ensemble import RandomForestClassifier
+>>> 
+>>> import os
+>>> 
+>>> iris = datasets.load_iris()
+>>> X = iris.data
+>>> y = iris.target
+>>> 
+>>> clf = RandomForestClassifier(n_estimators = 50, n_jobs=8)
+>>> model = clf.fit(X, y)
+>>> 
+>>> for i, t in enumerate(clf.estimators_):
+>>>     with open(os.path.join('/tmp/estimators', "iris-" + str(i) + ".txt"), "w") as file1:
+>>>         text_representation = tree.export_text(t, feature_names=iris.feature_names, show_weights=True, decimals=4, max_depth=sys.maxsize)
+>>>         file1.write(text_representation)
+```
+
+Once all the estimators are exported into `/tmp/estimators`, you can create a TAR archive, for example:
+```bash
+cd /tmp/estimators
+tar -czvf /tmp/iris.tgz .
+```
+
+Then you can use the RandomForestClassifier class to parse the TAR archive.
+
+```
+    import org.apache.commons.compress.archivers.tar.TarArchiveInputStream;
+    ...
+
+    TarArchiveInputStream tree = getArchive("iris.tgz");
+    final Classifier<Integer> decisionTree = RandomForestClassifier.parse(tree,
+                PredictionFactory.DOUBLE);
+```
 
 ## Testing
 Testing was done using sci-kit learn 1.1.3.

pom.xml

@@ -6,7 +6,7 @@
 
     <groupId>rocks.vilaverde</groupId>
     <artifactId>scikit-learn-2-java</artifactId>
-    <version>1.0.0-SNAPSHOT</version>
+    <version>1.0.1-SNAPSHOT</version>
 
     <name>${project.groupId}:${project.artifactId}</name>
     <description>A sklearn exported_text models parser for executing in the Java runtime.</description>
@@ -54,11 +54,28 @@
     </dependencyManagement>
 
     <dependencies>
+        <dependency>
+            <groupId>org.apache.commons</groupId>
+            <artifactId>commons-compress</artifactId>
+            <version>1.21</version>
+        </dependency>
+        <dependency>
+            <groupId>org.slf4j</groupId>
+            <artifactId>slf4j-api</artifactId>
+            <version>1.7.36</version>
+        </dependency>
+
         <dependency>
             <groupId>org.junit.jupiter</groupId>
             <artifactId>junit-jupiter</artifactId>
             <scope>test</scope>
         </dependency>
+        <dependency>
+            <groupId>org.slf4j</groupId>
+            <artifactId>slf4j-simple</artifactId>
+            <version>1.7.25</version>
+            <scope>test</scope>
+        </dependency>
     </dependencies>
 
     <build>

scikit-learn-2-java.iml

@@ -12,6 +12,8 @@
     </content>
     <orderEntry type="inheritedJdk" />
     <orderEntry type="sourceFolder" forTests="false" />
+    <orderEntry type="library" name="Maven: org.apache.commons:commons-compress:1.21" level="project" />
+    <orderEntry type="library" name="Maven: org.slf4j:slf4j-api:1.7.36" level="project" />
     <orderEntry type="library" scope="TEST" name="Maven: org.junit.jupiter:junit-jupiter:5.9.1" level="project" />
     <orderEntry type="library" scope="TEST" name="Maven: org.junit.jupiter:junit-jupiter-api:5.9.1" level="project" />
     <orderEntry type="library" scope="TEST" name="Maven: org.opentest4j:opentest4j:1.2.0" level="project" />
@@ -20,5 +22,6 @@
     <orderEntry type="library" scope="TEST" name="Maven: org.junit.jupiter:junit-jupiter-params:5.9.1" level="project" />
     <orderEntry type="library" scope="TEST" name="Maven: org.junit.jupiter:junit-jupiter-engine:5.9.1" level="project" />
     <orderEntry type="library" scope="TEST" name="Maven: org.junit.platform:junit-platform-engine:1.9.1" level="project" />
+    <orderEntry type="library" scope="TEST" name="Maven: org.slf4j:slf4j-simple:1.7.25" level="project" />
   </component>
 </module>

src/main/java/rocks/vilaverde/classifier/dt/DecisionTreeClassifier.java

@@ -1,15 +1,15 @@
 package rocks.vilaverde.classifier.dt;
 
-import rocks.vilaverde.classifier.Prediction;
-import rocks.vilaverde.classifier.Classifier;
 import rocks.vilaverde.classifier.Operator;
+import rocks.vilaverde.classifier.Prediction;
+
 import java.io.BufferedReader;
 import java.io.Reader;
 import java.util.Map;
 import java.util.Set;
 import java.util.Stack;
 
-public class DecisionTreeClassifier<T> implements Classifier<T> {
+public class DecisionTreeClassifier<T> implements TreeClassifier<T> {
 
   /**
    * Factory method to create the classifier from the {@link Reader}.
@@ -19,7 +19,7 @@ public class DecisionTreeClassifier<T> implements Classifier<T> {
    * @param <T> class
    * @throws Exception when the model could no be parsed
    */
-  public static <T> Classifier<T> parse(Reader reader, PredictionFactory<T> factory) throws Exception {
+  public static <T> DecisionTreeClassifier<T> parse(Reader reader, PredictionFactory<T> factory) throws Exception {
 
     try (reader) {
       DecisionTreeClassifier<T> classifier = new DecisionTreeClassifier<>(factory);
@@ -53,7 +53,7 @@ private DecisionTreeClassifier(PredictionFactory<T> predictionFactory) {
    * @return predicted class
    */
   public T predict(Map<String, Double> features) {
-    return findClassification(features).get();
+    return getClassification(features).get();
   }
 
   /**
@@ -64,13 +64,13 @@ public T predict(Map<String, Double> features) {
    */
   @Override
   public double[] predict_proba(Map<String, Double> features) {
-    return findClassification(features).getProbability();
+    return getClassification(features).getProbability();
   }
 
   /**
    * Find the {@link Prediction} in the decision tree.
    */
-  private Prediction<T> findClassification(Map<String, Double> features) {
+  public Prediction<T> getClassification(Map<String, Double> features) {
     validateFeature(features);
 
     TreeNode currentNode = root;

src/main/java/rocks/vilaverde/classifier/dt/PredictionFactory.java

@@ -1,11 +1,3 @@
-/////////////////////////////////////////////////////////////////////////////
-// PROPRIETARY RIGHTS STATEMENT
-// The contents of this file represent confidential information that is the
-// proprietary property of Edge2Web, Inc. Viewing or use of
-// this information is prohibited without the express written consent of
-// Edge2Web, Inc. Removal of this PROPRIETARY RIGHTS STATEMENT
-// is strictly forbidden. Copyright (c) 2016 All rights reserved.
-/////////////////////////////////////////////////////////////////////////////
 package rocks.vilaverde.classifier.dt;
 
 /**
@@ -16,6 +8,7 @@ public interface PredictionFactory<T> {
 
     PredictionFactory<Boolean> BOOLEAN = value ->  Boolean.valueOf(value.toLowerCase());
     PredictionFactory<Integer> INTEGER = Integer::valueOf;
+    PredictionFactory<Double> DOUBLE = Double::parseDouble;
 
     T create(String value);
 }

src/main/java/rocks/vilaverde/classifier/dt/TreeClassifier.java

@@ -0,0 +1,14 @@
+package rocks.vilaverde.classifier.dt;
+
+import rocks.vilaverde.classifier.Classifier;
+import rocks.vilaverde.classifier.Prediction;
+
+import java.util.Map;
+
+/**
+ * Implemented by Tree classifiers.
+ */
+public interface TreeClassifier<T> extends Classifier<T> {
+
+    Prediction<T> getClassification(Map<String, Double> features);
+}